Signal Complexity and Gaussian Process Models Approach for Bearing Remaining Useful Life Estimation

Abstract

Standard bearing fault detection features are shown to be ineffective for estimating bearings remaining useful life (RUL). In this paper we propose a new approach estimating bearing RUL based on features describing the statistical complexity of the envelope of the generated vibrations and a set of Gaussian process (GP) models. The proposed approach is shown to be sensitive to incipient condition deterioration which allows timely and sufficiently accurate estimates of the RUL. The proposed approach was evaluated on the data set comprising of 17 bearing runs with natural fault evolution.